Occupational solvent exposure during pregnancy and child behaviour at age 2
Bibliographic record
Abstract
OBJECTIVES: Many women who work during pregnancy are occupationally exposed to toxicants. The developing central nervous system is highly vulnerable to neurotoxicants such as solvents. Although the neurotoxicity of solvents to adults is well established, very few studies have examined their effects on children's behaviour following prenatal exposure. METHODS: Women from the Perturbateurs endocriniens: Étude Longitudinale sur les Anomalies de la Grossesse, l'Infertilité et l'Enfance (PELAGIE) mother-child cohort (including 3005 working women) were recruited in Brittany (France) between 2002 and 2006, at the beginning of pregnancy, to assess occupational exposure to solvents at that time. Child behaviour was documented at age 2 by mothers (n=1278) assessing components of attention deficit/hyperactivity, aggression, opposition and emotionality. We used a multiple linear regression analysis to evaluate the association between occupational solvent exposure and children's behaviour. Complementary sensitivity analyses allowed us to handle missing data, due mostly to attrition. RESULTS: 20% of women reported occasional exposure and 31% regular exposure to solvents. Children prenatally exposed were more likely to have higher scores of attention deficit/hyperactivity and aggression, and dose-response relations were observed. CONCLUSIONS: The dose-response effect and the high prevalence of children potentially exposed to solvents from their mother's workplace exposure underline the public health relevance of this result. Our results should be replicated in further studies designed to identify which solvents are most deleterious and to assess child behaviour at school age.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".